01 · terms
An AI tool that listens and watches a live interview and streams real-time answer suggestions to the candidate.
A real-time assistant that captures interview audio, reads the question off the screen, and streams a candidate-quality answer in milliseconds. Modern copilots like WinItAI route between multiple frontier models per question type and stay hidden from screen-share recording.
Separating who-said-what during multi-speaker interviews so the copilot knows which question belongs to which interviewer.
Speaker diarization is a speech-processing technique that segments audio by speaker identity. In multi-interviewer panel rounds, diarization tells the copilot 'recruiter asked X, hiring manager asked Y' so the answer style can be tailored.
Reading text off the screen at higher granularity than a single video frame, used to capture interview questions reliably.
Sub-frame OCR samples and reads pixels between video frames so it captures questions even when the interview platform tries to obfuscate text. WinItAI's average sub-frame OCR confidence is 0.972.
The technique that keeps a copilot's overlay visible to the candidate but invisible to screen-share and recording paths.
Screen-share evasion combines hidden-window rendering, anti-pattern eye-tracking, accessibility-API cloaking, and per-frame canvas redraw. WinItAI uses five layers and has been benchmarked against 17 recorders with zero detections.
Automatically picking the best AI model for each question type — behavioral, system design, coding, market sizing.
Model routing dispatches each interview question to the model best suited for that question type. WinItAI routes Claude Opus 4 for behavioral STAR answers, GPT-5 Pro for system design, DeepSeek V4 for competitive coding, and so on across 11+ providers.
Time from when the interviewer finishes speaking to when the first answer token streams to the candidate.
End-to-end latency is the full pipeline: audio capture, transcription, model routing, generation, and stream. WinItAI's average is 116 ms, with a published p99 under 200 ms.
The latency that 99% of requests stay under — a stricter measure than average.
P99 latency is the slowest 1% of requests. It captures tail behavior — the worst case a candidate might experience mid-interview. WinItAI publishes a sub-200 ms p99 latency target.
The answer appears token-by-token as it is generated, not as a single block at the end.
Streaming lets the candidate start reading the answer the moment generation begins, eliminating the round-trip wait. Combined with low end-to-end latency, this is what makes a copilot feel instant.
Running the copilot on a separate device while the candidate takes an audio-only phone screen.
Phone interview mode is used when there is no shared screen — the candidate places a laptop or second monitor next to their phone, and the copilot listens via microphone and streams answers to the laptop screen.
A web-based collaborative coding interview platform widely used by tech companies.
CoderPad provides a shared editor, multi-language compilation, and interviewer controls for live coding rounds. WinItAI integrates natively with CoderPad's editor canvas to capture the problem and stream solutions in real time.
A coding challenge and interview platform that hosts both async assessments and live coding interviews.
HackerRank is one of the largest coding interview platforms. WinItAI supports both its live interview mode and its async assessment mode with sub-frame OCR active on the editor.
A coding assessment and interview platform, often used for the Industry Coding Framework score.
CodeSignal hosts both the General Coding Framework assessment and live interviews. WinItAI integrates natively across both modes.
Situation, Task, Action, Result — the canonical structure for behavioral interview answers.
The STAR method gives behavioral answers a clean four-part structure that interviewers can score quickly. WinItAI routes behavioral questions to Claude Opus 4, which produces natural STAR answers grounded in the candidate's resume.
An interview round where the candidate designs the architecture for a large-scale system on a whiteboard or shared canvas.
System design interviews test how a candidate scales, partitions, and operates a real system. WinItAI handles the capacity math, reference architectures, and trade-off framing in real time so the candidate can drive the discussion in their own words.
An async coding or design exercise the candidate completes outside of a live interview window.
Take-home assessments range from one-hour coding problems to multi-day full-stack builds. WinItAI is most effective on live rounds; for take-homes the candidate-driven workflow is preferred.
An interview at Facebook (Meta), Amazon, Apple, Netflix, or Google — generally the highest-bar tech loops.
FAANG interviews typically include 4-6 rounds covering coding, system design, behavioral, and bar-raiser. WinItAI is tuned for the depth and pace of these loops.
An Amazon interview round designed to keep the company's hiring bar high — typically the toughest round of the loop.
The bar raiser is run by an interviewer outside the hiring team whose job is to veto candidates who do not raise the bar. WinItAI handles the bar raiser the same way as senior coding loops, with extra emphasis on Amazon Leadership Principles when the resume context indicates Amazon.
An interview round focused on past behavior as a predictor of future performance — STAR answers, leadership stories.
Behavioral interviews probe how the candidate has actually behaved in past situations. WinItAI grounds behavioral answers in the candidate's uploaded resume so the stories feel native, not generated.
A real-time coding round where the candidate writes code on a shared editor while the interviewer watches.
Live coding interviews test problem-solving under observation. WinItAI streams the optimal solution with complexity analysis while the candidate types and narrates, keeping the conversation candidate-driven.
A multi-round interview day, typically with 4-6 back-to-back rounds covering different competencies.
Onsite loops are the gauntlet — coding, system design, behavioral, debugging, and lunch chat. WinItAI's session continuity carries context from one round to the next so the candidate's story stays consistent.
Back-of-envelope math during system design interviews — QPS, storage, bandwidth.
Capacity estimation establishes the scale of the system being designed. WinItAI computes defensible numbers (1B users → ~10K QPS → ~5 PB storage growth/year) so the candidate can write them on the whiteboard with confidence.
Splitting a database or service across multiple instances by some key, used in system design discussions.
Sharding is the canonical horizontal-scaling technique. WinItAI surfaces the shard key choice (user_id, tenant_id, geo) and the trade-offs (hot shards, rebalancing) for senior design rounds.
How thoroughly an AI interview copilot stays hidden from screen recording, telemetry, and process detection.
Stealth posture is the security model of an interview copilot. WinItAI's posture combines five cloaking layers and has been benchmarked against 17 screen-recorders with zero detections. Stealth is necessary because most interview platforms now run pattern-detection on shared screens.
Continuously repainting the overlay so screen-recorders cannot capture a stable frame to detect.
Per-frame canvas redraw forces screen-capture to see motion at every frame, defeating fingerprint-based detection that looks for a static overlay. It is one of WinItAI's five cloaking layers.
Preventing the OS accessibility API from exposing the copilot to screen-readers or telemetry tools.
Many OS-level recording and telemetry tools query the accessibility API to enumerate windows. Accessibility API cloaking removes the copilot from that enumeration so the OS reports it as not present.
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